Enhanced countermeasures for all-digital line-of-sight (LOS) processor

ABSTRACT

An all-digital line-of-sight (LOS) process architecture addresses the size, weight, power and performance constraints of a receiver for use in semi-active or active pulsed electromagnetic (EM) targeting systems. The all-digital architecture provides a platform for enhanced techniques for sensitive pulse detection over a wide field-of-view, adaptive pulse detection, LOS processing and counter measures.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a line-of-sight (LOS) processor architecturefor a receiver for use in semi-active or active pulsed electromagnetic(EM) targeting systems, and more particularly to an all-digitalarchitecture that addresses the size, weight and power constraints oftypical applications and provides a platform for enhanced techniques forsensitive pulse detection over a wide field-of-view, adaptive pulsedetection, LOS processing and counter measures.

2. Description of the Related Art

Certain aircraft, missiles and smart projectiles include a receiver thatallows the platform to receive and process EM pulses, typically in thenear IR, transmitted by a known source and returned off of a target. Thereceiver may be fixed to look along the line-of-sight (LOS) of theplatform or gimbaled to look along a receiver LOS relative to theplatform LOS. If the source is remotely located the system is referredto as ‘semi-active’ whereas if the source is co-located on the platformwith the receiver the system is referred to as ‘active’. The receivermay perform many different functions including acquisition and tracking,identification, active countermeasures and aimpoint selection. A corecapability required to support each of these functions is to reliablyand accurately detect the presence of EM pulses returned from a realtarget from amidst natural clutter and active jamming and to measure theline-of-sight (LOS) from the platform to the target. Errors in detectionor LOS processing can cause mission failure.

As shown in FIG. 1, a typical receiver 10 includes a quad detector 12that detects incident radiation (e.g. transmitted pulses reflected off atarget, pulses transmitted to actively jam the receiver and backgroundnoise) and generates analog signals 14, a bank of pre-amps 15 thatamplify the analog signals 14 and a LOS processor 16 that detects“pulses”, calculates a LOS to a target and performs additionalprocessing to generate a target report 18 that is passed on to a commandand guidance processor 20 to guide the platform to the target.

LOS processor 16 is based on a mixed analog/digital architecture andfunctionality. The processor can be roughly divided into threefunctional areas: pulse detection 22, LOS calculation 24 and DSPpost-processing on the pulses and LOS 26. Pulse detection 22 is astandard analog operation in which an analog sum channel is thresholdedto satisfy a Constant False Alarm Rate (CFAR) criteria evaluated over afixed period (typically multiple EM pulse repetition intervals for theknown source) to identify candidate pulses. Parameters such as rise/falltimes and pulse width are extracted from each candidate pulse andevaluated to independently determine whether the candidate pulse is areal target pulse. Upon validation of a target pulse, a sample and holdcircuit samples the analog signals once near the peak and analogcircuits combine the peak values to provide the LOS calculations 24.Ideally, the analog signals are sampled at the peak but there is somedegree of alignment error between peak detection and the delayed analogsignals. The DSP post-processing 26 performs time-correlation on thevalidated pulses to determine if they occur at the expected repetitionrate of the transmitted EM pulses and to determine if the LOScalculations are acquiring and tracking a target.

SUMMARY OF THE INVENTION

The present invention provides an all-digital LOS processor architecturewith improved pulse detection and LOS calculation performance at reducedsize, weight and power that provides a platform for enhanced techniquesfor sensitive pulse detection over a wide field-of-view, adaptive pulsedetection, LOS processing and counter measures.

In a first embodiment, the all-digital LOS processor includes aplurality of A/D converters for converting analog signals from amulti-channel detector into a plurality of digital signals on respectiveindividual channels. A digital summer sums all the digital signals toproduce a digital signal on a primary sum channel. The architecture isexpandable to sum different subsets of less than all the individualdigital signals to produce digital signals on respective secondary sumchannels. The secondary sum channels fill the gap between the field ofview (FOV) of the individual channels and the primary sum channel.

Depending on system performance and processor cost considerations, pulsedetection can be performed on all of these channels or different subsetsof the channels as long as detection is performed on multiple channelsof which at least one channel is a sum channel. In an optimalperformance configuration, digital pulse detectors compare sampleamplitudes on the individual and the primary and secondary sum channelsto respective detection thresholds to detect pulses with sensitivityover a wide FOV. In another configuration, pulse detection is performedon the primary and secondary sum channels and not the individualchannels. In yet another configuration, pulse detection is performed ononly the individual and secondary channels. In even yet anotherconfiguration, pulse detection is performed on only the secondarychannels.

The detection process is preferably configurable to allow any digitalchannel to be activated or deactivated interactively based on an apriori LOS estimate of the target or on a LOS estimate with time and theobserved (measured) EM signal parameters. Down selection to the subsetof channel(s) expected to have the highest SNR can reduce susceptibilityto false alarms and jammers. Typically the individual channels will bedeactivated from detection upon establishment of signal track and onlythe primary sum channel will remain active for detection. Alternately,the sensor on the platform may be steered so that an individual channelor a secondary sum channel, which exhibit higher peak SNR than theprimary sum channel albeit for a narrower FOV, is pointed at theestimated position of the target. If an individual channel isdeactivated from pulse detection, the channel is still sampled andstored for LOS processing.

Upon detection of a pulse by any one of the active detectors, a memorycontroller stores a plurality of samples for each individual channel ina memory. An embedded processor processes the samples for the individualchannels to calculate a LOS to a target. Pulse detection is improved bydetecting pulses on all of the channels or subsets of channels, not justa primary sum channel. The LOS calculation is improved by processing theamplitudes of multiple samples for each individual channel to betterestimate a peak value. Except for the A/D converters, the rest of thedigital processing may be integrated in a single processor. Thisall-digital architecture provides the platform on which additionaldigital signal processing can be implemented to further improve pulsedetection and LOS calculations and to implement effectivecountermeasures.

Detection performance may be enhanced by matched filtering the digitalsignals. The filters are ‘matched’ to the approximately knowncharacteristics of the EM pulses. These filters may adapt as theprocessor tracks a target to further boost performance.

Detection performance and processor throughput may be further enhancedby performing a time-correlation of the detected pulses to the expectedrepetition rate/time-interval of the EM pulses prior to applying thepulse discrimination logic to each detected pulse. This removes a largenumber of spurious pulses prior to the more computationally intensivepulse discrimination. This also allows for individual pulse parameters,joint pulse parameters and time-correlation errors to be combined into amore complete association error for acceptance or rejection of multiplepulses as a pulse train. The processor suitably identifies the strongestSNR channel (individual or sum) for each detected pulse and then usesthat channel's pulse for both time-correlation and pulse discrimination.The strongest channel is used to determine the curve fitting parametersto estimate the peak from the stored amplitude samples with theparameters being applied to the other individual channels.

In a second embodiment, the all-digital LOS processor includes aplurality of A/D converters for converting analog signals from amulti-channel detector into a plurality of digital signals on respectiveindividual channels. One or more digital summers sum all and/or subsetsof the digital signals to produce digital signals on a primary sumchannel and secondary sum channels, respectively. At least one digitalpulse detector on a sum channel, and preferably all channels, comparessample amplitudes of the digital signals to a detection threshold todetect pulses including “pulse” samples with amplitudes above thethreshold and “noise” samples with amplitudes at or below the threshold.Upon detection of a pulse, a memory controller stores a plurality ofsamples for each individual channel in a memory. An embedded processorprocesses the samples for the individual channels to calculate a LOS toa target. At least one threshold processor on the sum channel,preferably all channels, processes the noise samples to estimate noiseand update the detection threshold as a function of the noise. Theprocessor performs the calculation and updates the detection thresholdmultiple times in the known period between EM pulses. The detectionthreshold is suitably updated as a function of both the noise and a CFARfactor. The CFAR factor may be evaluated on a fixed period as isconvention. To improve responsiveness, the CFAR factor is preferablyincreased as soon as a max rate is exceeded within the fixed period andthe period and count reset. The CFAR factor may be established jointlyfor all channels or independently for each channel allowing anothertechnique for desensitizing the detection on individual channels asopposed to turning a channel off. The combination of the modified CFARfactor and the noise statistics makes the individual and sum channeldetection thresholds more responsive to actual real-time conditions oneach channel, thus improving the detection of real EM pulses andsuppressing the detection of noise or jamming pulses. Except for the A/Dconverters, the rest of the digital processing may be integrated in asingle processor.

In a third embodiment, the all-digital LOS processor includes aplurality of A/D converters for converting analog signals from amulti-channel detector into a plurality of digital signals on respectiveindividual channels. One or more digital summers sum all and/or subsetsof the digital signals to produce digital signals on a primary sumchannel and secondary sum channels, respectively. At least one digitalpulse detector on a sum channel, and preferably all channels, comparessample amplitudes to a detection threshold to detect pulses includingpulse samples with amplitudes above the threshold and leading andlagging samples with amplitudes below the threshold. Upon detection of apulse, a memory controller stores a plurality of samples for eachindividual channel in a memory. A processor determines a strongest SNRchannel from the individual and sum channels, estimates a pulse widthfor the strongest channel, selects the time samples to be processed anddetermines the logic and scale factor to fit a curve to the selectedtime samples for all channels based on the pulse width estimate. Theprocessor applies the logic and scale factor to the selected timesamples in each individual channel to estimate a peak amplitude and thencalculates a LOS to the target from the estimated peak amplitudes. If achannel is dropped or saturates, the processor can adjust the estimationand LOS calculations accordingly. Although typically not stored inmemory, the samples for the sum channel(s) can be recreated from theindividual channel if a sum channel is the only or strongest channel fora given pulse. Except for the A/D converters, the rest of the digitalprocessing may be integrated in a single processor.

Pulse width is preferably computed as a function of the amplitudes ofthe plurality of stored pulse and leading/lagging samples for thestrongest SNR channel. Conventional pulse width estimates, for pulsediscrimination not LOS calculation, typically count the number ofdetected samples and multiple by the sampling period. This approach doesnot provide a sufficiently accurate or stable estimate of the pulsewidth. To determine pulse width, we identify all samples that lie abovea threshold measured down from the maximum amplitude sample (e.g. 6 dBdown from the max) and at least one sample on either side that liesbelow the threshold. The pulse width is measured as the power in thesesamples divided by the peak energy.

The curve fit may be a conventional linear curve fit to an N^(th) orderpolynomial or an alternate linearization of a curve fit algorithm. Forthe same number of calculations, the latter approach provides a moreaccurate estimate of the peak. Essentially, the latter approach looks atthe two samples with the largest amplitudes in each channel for a givenpulse. If one amplitude is considerably greater than the other, thelarger amplitude is probably very close to the peak amplitude of theunderlying analog signal. In this case, either the larger amplitude isaccepted as the estimate of the peak or increased by a small scalefactor. If the two amplitudes are close, the peak amplitude of theunderlying analog signal lies approximately at a mid-point in timebetween the two samples. In this case, the larger amplitude is adjustedby a larger scale factor. The strongest SNR channel is used to selectthe two time samples that will be used for all channels, to determine ascale factor that is used for all channels and to control whether anaveraging logic is applied to a given pulse or the above curve fit logicis applied.

In a fourth embodiment, the all-digital LOS processor includes aplurality of A/D converters for converting analog signals from amulti-channel detector into a plurality of digital signals on respectiveindividual channels. One or more digital summers sum all and/or subsetsof the digital signals to produce digital signals on a primary sumchannel and secondary sum channels, respectively. At least one digitalpulse detector on a sum channel, and preferably all channels, comparessample amplitudes to a detection threshold to detect pulses. Upondetection of a pulse, a memory controller stores a plurality of samplesfor each individual channel in a memory. A filter removes pulses thatoccur at frequencies greater than an octave above a known repetitionrate of the EM pulses. A processor processes the samples for theindividual channels to calculate a LOS to a target. The removed pulsesare typically “jammer” pulses transmitted to prevent the LOS processorfrom acquiring and tracking the target. The primary goal of the jammerpulses is to overwhelm the processor and memory, not to obfuscate thereal EM pulses. Therefore it is useful to aggressively identify andremove jammer pulses as soon as possible to remove them from memory andto avoid applying the pulse discrimination logic to such pulses. A notchfilter can be configured to remove either single-frequency orpseudo-random jammer pulses that lie more than an octave above therepetition rate of the EM pulses. In some instances, jammer pulses thatoccur at approximately the same time as an EM pulse cannot be removed bythe notch filter and appear in the time-correlation track gate with theEM pulse. The processor suitably selects the pulse that lies closest tothe middle of the track gate, the pulse that occurs either before orafter any other pulses within the track gate or the pulse with thelargest amplitude. Once the target is acquired, the track gate isnarrowed, which helps to eliminate jammer pulses not removed by thenotch filter.

The notch filter is particularly effective when used as a front-endprocess for adaptive thresholding based on both the CFAR and channelnoise. If the notch filter effectively removes jammer pulses, thethreshold processor will optimize the threshold to detect EM pulses toacquire and track the target. If the notch filter cannot remove jammerpulses, the threshold process will react very quickly (e.g. much lessthan one repetition pulse interval of the EM pulses), treating thejammer pulses as noise or false-alarms, to raise the threshold toprevent further detection of jammer pulses.

In a fifth embodiment, the all-digital LOS processor incorporates theall-digital architecture to detect pulses on all of the channelsincluding the individual and primary and any secondary channels,allowing for selective activation and deactivation of any of thechannels, and save multiple samples for each detected pulse, the notchfilter to remove jammer pulses, adaptive thresholding based on bothchannel noise and CFAR to better discriminate pulses and enhanced LOSprocessing on the multiple samples to better estimate the peakamplitudes. The notch filtering, time-correlation, pulse discriminationlogic and LOS processing to determine a curve fit are all preferablyperformed on the ‘strongest’ channel for each detected pulse.

These and other features and advantages of the invention will beapparent to those skilled in the art from the following detaileddescription of preferred embodiments, taken together with theaccompanying drawings, in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1, as described above, is a block diagram of an analog semi-activelaser (SAL) LOS processor;

FIG. 2 is a diagram of an all-digital LOS processor in accordance withthe present invention;

FIG. 3 is a functional block diagram of the all-digital LOS processor;

FIG. 4 is a block diagram illustrating the four channels of the LOSprocessor for a quad-detector;

FIGS. 5 a, 5 b and 5 c, are SNR plots for the primary sum channel, fourindividual channels and a composite of the individual, primary and foursecondary channels for a quad-detector, respectively;

FIG. 6 is a diagram of an FIR matched filter;

FIGS. 7 a and 7 b are a flow diagram and a plot illustrating PRI/timecorrelation for acquiring pulse sequences for a known laser source.

FIGS. 8 a through 8 c are diagrams illustrating adaptive thresholdingfor pulse detections as a function of real-time noise statistics andCFAR as pulses are detected;

FIG. 9 is a block diagram illustrating the real-time calculation of theadaptive threshold;

FIG. 10 is a block diagram illustrating the calculation of the CFAR aspulses are detected;

FIGS. 11 a and 11 b are a block diagram and illustration of the LOScalculation of LOS based on four good channels

FIGS. 12 a and 12 b illustrate an energy based approach for pulse widthestimation;

FIG. 13 is a diagram of a curve fit technique for estimating the peakpulse value; and an alternative calculation of LOS enabled by theall-digital architecture if a channel is bad;

FIG. 14 is a diagram illustrating the LOS calculation assuming onechannel is missing;

FIG. 15 is a diagram for estimating the peak pulse value in the event ofchannel saturation;

FIG. 16 is a flow diagram of an acquisition and track logic sequenceincluding countermeasures to eliminate jamming pulses;

FIGS. 17 a and 17 b are diagrams illustrating the high pulse repetitionfrequency (HPRF) notch filter for eliminating jamming pulses forsingle-frequency and pseudo random jamming sequences, respectively; and

FIGS. 18 a and 18 b are a more detailed flow diagram of the logicsequence and a plot of gate width as the processor tracks.

DETAILED DESCRIPTION OF THE INVENTION

The present invention describes an all-digital architecture for aline-of-sight (LOS) processor on a receiver for use in semi-active oractive pulsed electromagnetic (EM) targeting systems. The all-digitalarchitecture addresses the size, weight, power and performanceconstraints of typical applications and provides enhanced techniques foradaptive detection, LOS processing and counter measures. The receiver ismounted on a platform such as a missile, smart, projectile or bomb toreceive EM pulses from a known source on the same or different platformreflected off a target. The receiver may be fixed to look along theline-of-sight (LOS) of the platform or gimbaled to look along a receiverLOS relative to the platform LOS. The EM pulses can be electro-optic EO(SWIR, NIR, MWIR, LWIR), RF (radio frequency) or MMW (millimeter wave).The approximate pulse shape (e.g. width) and repetition rate (pulseinterval) is known to the receiver.

All-Digital LOS Processor Architecture

As shown in FIG. 2, a receiver 50 includes a quad detector 52 thatdetects incident radiation (e.g. transmitted pulses reflected off atarget, pulses transmitted to actively jam the receiver and backgroundnoise) and generates analog signals 53, a bank of pre-amps 54 thatamplify the analog signals 53 and A/D converters 55 that convert analogsignals 53 to digital signals 56 on individual channels A, B, C and D.The digital signals comprise a sequence of samples each having a sampleor time index and an amplitude. The samples may be referred to incontext as “samples”, “time samples” or “amplitude samples” depending onwhether the relevant property is the time-index or the amplitude. Adigital LOS processor 57 detects “pulses”, calculates a LOS to a targetand performs additional processing to generate a target report 58 thatis passed on to a command and guidance processor 60 via digital bus 62to guide the platform to the target. The processor provides a gain thatis passed back to the pre-amps via digital bus 64. This figureillustrates that all of the pulse detection and LOS processes, exceptfor the front-end A/D conversion, is digital and may be implemented in asingle processor 57 that includes memory. Receiver 50 is typicallymounted on a platform such as a missile, bomb or smart projectile thatis guided to a target. The various digital functions may be separatedinto different processors. The all-digital architecture is smaller,lighter weight and uses less power than the standard LOS processor. Theall-digital architecture provides a platform for implementing digitalprocessing techniques to enhance the performance of the key pulsedetection and LOS calculation and to provide additional functionality.

Different functional and architectural features of the all-digital LOSprocessor 57 are illustrated in FIGS. 3 and 4. The digital signals 56 onindividual digital channels A, B, C and D are optionally match filtered70 (matched filter 90) to improve the signal-to-noise ratio (SNR) ofeach channel (described in more detail in FIG. 6). The approximate shape(e.g. width) of the transmitted pulses is known. Typically, the receiverwill be provided with the pulse specification for the class oftransmitter. As the target is acquired and tracked, the actual pulsewidth can be estimated and used to update the filter coefficients tofurther improve the matched filter. Matched filtering is optional andthe filtered data is preferably only used for the initial detection orpulse capture. The raw digital data (samples with a time index andamplitude value) is stored and used for processing.

The filtered digital channels are digitally summed 72 (digital summer92) to form a one or more sum channels. All the digital signals may besummed to produce a “primary” sum channel. In addition to or instead ofthe primary sum channel, different subsets of less than all theindividual digital signals may be summed to produce different digitalsignals on respective secondary sum channels. For example, in aquad-detector the four different pair combinations (A+B, B+C, C+D, D+A)of channels A, B, C and D produce four different secondary sum channels.The primary and secondary sum channels fill the gaps between the fieldof view (FOV) of the individual channels. The sums progress fromindividual channels (no sum) with the smallest FOV coverage per channelbut maximum SNR, to secondary sum channels to increase the FOV coverageper channel but with lower SNR to the primary sum channel that coversall of the FOV but has the lowest SNR. The individual channels formlobes in angle coordinates that have a high SNR at the center of a lobebut lower SNR as the target gets away from the center. The secondary sumchannels fill in the SNR nulls along the AZ and EL axis and the primarysum channel fills in the null at boresight (AZ=0, EL=0).

Depending on system performance and processor cost considerations, pulsedetection can be performed on all of these channels or different subsetsof the channels. In an optimal performance configuration, digitaldetectors compare sample amplitudes on the individual and the primaryand secondary sum channels to respective detection thresholds to detectpulses. In another configuration, pulse detection is performed on theprimary and secondary sum channels and not the individual channels. Inyet another configuration, pulse detection is performed on only theindividual and secondary channels. In even yet another configuration,pulse detection is performed on only the secondary channels. Note, ifthe processor is configured in such a manner that the primary sumchannel or secondary sum channels are not used to detect pulses than thedigital summer(s) that create these sum channels are not needed.

For the primary sum channel, a signal level that would produce a SNRequal to 1.0 at the Field Of View edge and a SNR of 2.0 at the center ofthe FOV is shown in the table for the same signal strength andindividual channel noise.

FOV Edge FOV Center Highest SNR a) Primary 1 2.0 2.0 b) Individual 1.51.4 4.0 c) Primary + indiv 1.5 2.0 4.0 d) Secondary 1.8 1.7 2.8 e)Primary + Secondary 1.8 2.0 2.8 f) Secondary + indiv 1.8 1.7 4.0 g)Primary + Secondary + indiv 1.8 2.0 4.0

Each of the individual filtered channels and the sum channel(s) areindependently processed to detect pulses 74 (threshold pulse detector94). Pulses are detected by comparing the sample amplitudes to athreshold; samples having amplitudes that exceed the threshold indicatea possible pulse and are labeled “pulse” samples while samples havingamplitudes at or below the threshold are labeled “noise” samples. Thefew noise samples in front of a pulse are called “leading” samples whilethe few noise samples trailing the pulse are called “lagging” samples.The leading and lagging samples may or may not include pulse energydepending on the pulse width and threshold. As will be described in moredetail with reference to FIGS. 8-10, the threshold for each individualchannel and the sum channel(s) are preferably computed 76 as a functionof both a CFAR factor and the channel noise and DC (mean value)(noise/DC processor 96) derived from the noise samples. The CFAR factorand command may be computed for all channels together or for eachchannel individually.

Upon detection of a pulse by any of the channels, multiple samples ofthe raw digital data with time tags are stored 78 (buffer control logic98) in a pulse buffer 80 for each of the individual channels. The rawdigital data is delayed in shift registers 81 so that it is time-alignedto the pulse detection. For each pulse, “pulse” samples 82 above athreshold 83 and “leading” samples 84 and “lagging” samples 85 below thethreshold are stored. Each sample is tagged with a time of arrival. Foreach detected pulse, one of the sum or individual channels is designatedas the ‘strongest’ channel based on SNR. The digital data for the sumchannel(s) are not typically stored in the pulse buffer. This data canbe recreated from the individual channels if needed. The sum channeldata may be needed if a sum channel is the strongest channel in order toperform time-correlation and pulse discrimination and to determine thecurve fit for peak estimation.

The strongest channel is the channel with the highest SNR, also thechannel that typically detects a pulse first. As shown in FIGS. 5 a-5 c,the SNR 86 of each individual channel, the SNR 87 of the primary sumchannel and the SNR 89 of the secondary sum channels are strongest indifferent regions (e.g. FOVs) of the quad-detector. Depending on thecurrent LOS to the target, the returned EM pulses will be detected morestrongly in one region. A simple and effective way to assign thestrongest SNR channel is to select the channel with the maximum sampleamplitude for a given pulse or assign the sum channel when thedifference between the maximum channel amplitude and the minimum channelamplitude is below a threshold (all channel amplitudes are nearlyequal). Once the receiver locks-on to and tracks the target, thereceiver platform should point at the target and the sum channel wouldbe expected to be the strongest channel. Therefore, the performance ofpulse detection on all channels and the selection of a strongest channelare most important for initial acquisition and reacquisition should thereceiver break track. In some gimbaled systems, even after the receiverlock-on and enters track mode, the receiver could be offset from thetarget LOS to point a higher SNR channel at the target than the primarychannel.

The detection process is preferably configurable to allow any digitalchannel (e.g. digital pulse detector) to be activated or deactivatedinteractively by the processor based on an a priori LOS estimate of thetarget or LOS estimates with time and the observed (measured) EM signalparameters with respect to LOS of the receiver platform to the target.Down selection to the subset of channel(s) expected to have the highestSNR can reduce susceptibility to false alarms and jammers. Typically theindividual channels will be deactivated from detection uponestablishment of signal track (e.g. align boresight of the receiver withthe LOS of the platform at the target) and only the primary sum channelwill remain active for detection. Alternately, the sensor on theplatform may be steered so that an individual channel or a secondary sumchannel, which exhibit higher peak SNR than the primary sum channelalbeit for a narrower FOV, is pointed at the estimated position of thetarget (e.g. offset receiver boresight from the platform LOS andtarget). If an individual channel is deactivated from pulse detection,the channel is still sampled and stored for LOS processing.

Many systems have an estimate of where the target is located and wherethe sensor host platform is located as well as the Roll, Pitch & Headingof the host platform and EM sensor. Together these provide an estimateof where in the Azimuth and Elevation FOV of where the signal ofinterest (target) is located and therefore which channels or sumchannels should have the best SNR. Some systems (e.g. gimbaled systems)are able to steer the sensor and point the highest sensitivity point inthe sensor toward the expected target location.

Sometimes this estimate is somewhat coarse. But if for instance thetarget is believed to be down then the platform can point the sensorslightly up to ensure that the target appears at a negative elevationangle and thus activate the dual sum channel that points down and thetwo individual channels that point down but deactivate the otherchannels until track is established.

If the system does not have this information a priori then the detectionprocess can still be modified after track is established to select onlythe best combination of channels and sum channels to minimize falsealarms and jammer susceptibility. Normally after track is establishedthe target is centered in the detector FOV and the primary channelbecomes the best SNR choice and the other channels can be turned off.Before the signal reaches a sufficient SNR to track on the sum channel atechnique called “offset track” can be used when the sensor can beoffset relative the direction of platform motion or the optics of thesensor has a wide FOV (with lower LOS accuracy).

An embedded signal processor 88 processes the stored amplitude samplesto perform a time-correlation of the expected pulse rate interval (PRI)to the detected pulses, determine a strongest SNR channel for each pulseand apply pulse discrimination logic (PDL) to the pulses (describedbelow with reference to FIGS. 7 a and 7 b) and finally to perform a LOScalculation on the multiple amplitude samples for the surviving EMpulses (described below with reference to FIGS. 11 through 15). Theresults of operating from an all-digital platform that facilitatesadditional signal processing include a more accurate and reliabledetection and capture of EM pulses and a more accurate estimate of thepeak value of the underlying analog signals from which to calculate theLOS. Consequently, target report 58 passed onto the command and guidanceprocess is both more accurate and more reliable. As mentionedpreviously, for clarity the functionality and digital components areillustrated separately but are all suitably integrated into a singleembedded signal processor 88.

As shown in FIG. 6, an exemplary embodiment of matched filter 90comprises a 4-tap FIR filter. The filter coefficients 100 are stored inmemory 102 in embedded processor 88. The initial coefficients aredictated by identification of the preferred source. The exactcoefficients will vary somewhat for each specific source and change withtemperature and other environmental factors. As pulses are captured andtracked, the embedded processor can adjust the filter coefficients tothe characteristics of the received pulses. As the digital samples 103for each channel pass through the digital shift registers 81, the sampleamplitudes 104 are digitally multiplied (digital multiplier 106) by therespective filter coefficients. Digital summer 107 sums the weightedsample amplitudes to output the filtered digital signal that is passedto the threshold detector 94 (shown here with pulse buffer 80 integratedwith embedded processor 88). When matched filter 90 coincides with an EMpulse, the amplitude of the output pulse 108 is increased. The effect isto increase the SNR of pulse detection.

Pulse detection and capture merely identifies digital samples abovethreshold which might be pulses. Additional processing is required toseparate true EM pulses returned from a target from false pulses due tobackground noise or active jamming. As will be described subsequently inreference to FIGS. 16-18, a notch filter (not shown) is used to removepulses at repetitions frequencies more than an octave above that of theEM pulses. As shown in FIG. 7 a, with knowledge of the EM source's pulserepetition interval (PRI) and the pulse shape, the embedded processor 88performs a time-correlation 110 of the expected PRI 112 to the sequenceof detected pulses 114 using track gates 115 to downselect to one ormore candidate sequences, determines the strongest SNR channel for eachpulse 109 and applies pulse discrimination logic (PDL) 116 to the pulsese.g. pulse width, rise and fall times, amplitude, LOS calculation etc.The processor evaluates both the time-correlation errors anddiscrimination errors to acquire a target sequence. Once the target isacquired, the processor enters track mode 118. The processor places andupdates 120 track gate 115 (e.g. narrows the gate) to capture the nextEM pulse. The placement and narrowing of the gate rejects false pulseswithout performing further correlation. If the track ‘breaks’, theprocessor will widen the track gate and restart the time-correlation.The initial width of the track gate is suitably set by the worst casetime error of the EM source.

The all-digital LOS processor provides for distinct processingadvantages over its mixed analog/digital counterpart. First, because theall-digital LOS processor detects pulses on multiple channels (e.g. allindividual and sum channels or subsets thereof), allowing for selectiveactivation and deactivation of any of the channels, the time-correlationand PDL can be performed on a strongest SNR channel for each detectedpulse. As shown in FIG. 7 b, the first detected EM pulse 114 isstrongest in Channel A, the second detected EM pulse is strongest inChannel B and the third, fourth and fifth pulses are strongest in theprimary Sum Channel. This example is merely illustrative to show thebenefits of performing pulse detection on all channels and thenselecting the strongest channel to process. One can readily see thatprior to acquisition of the target track the strongest EM pulse returncould jump from channel-to-channel. The time-correlation and PDL is thusenhanced by processing the strongest channel for each possible pulse.Second, because the all-digital LOS process stores multiple samples foreach detected pulse the time-correlation can be performed before thePDL. This removes a large number of spurious pulses prior to the morecomputationally intensive pulse discrimination. This also allows forindividual pulse parameters (e.g. LOS, PW, Rise Time, Fall Time, PulseAmplitude and Delta Time from track gate center), joint pulse parametersand time-correlation errors to be combined into a more completeassociation error for acceptance or rejection of multiple pulses as apulse train. Joint parameters may look at a uniformity metric of theindividual pulse parameters over the pulse train e.g. variance of thepulse width, rise/fall times, amplitudes, LOS calculations etc. Theincorporation of time-correlation and joint errors into the metric todeclare a pulse a true EM pulse or to reject it as a false pulse has thedual beneficial effect of removing strong yet false pulses and retainingweak yet true pulses. The delay to store and process the samples inreverse order (time-correlation before PDL) is a small fraction of an EMpulse period. This delay is known and can be output with the LOScalculations if desired. The result is that the all-digital LOSprocessor performs much better than its mixed analog/digital counterpartat efficiently eliminating false pulses and quickly acquiring andlocking-on to true EM pulses.

The all-digital LOS processor architecture provides a platform in whichEM pulses are detected and captured with greater efficiency andreliability than the mixed analog/digital architecture. Multiple samplesare stored for each of the EM pulses to further facilitate a moreaccurate LOS calculation. The platform also facilitates additionaldigital signal processing to further improve pulse detection in noisyenvironments and when encountering active countermeasures.

Adaptive Pulse Detection Based on Channel Noise and CFAR

The initial detection and capture of pulses is important to the overallperformance of the LOS processor. Pulse detection is based on a simplecomparison of the sample amplitude to a threshold. If the amplitudeexceeds the threshold the sample is declared a “pulse” sample, otherwiseit is a “noise” sample. Although the downstream time-correlation and PDLcan extract sequences of EM pulses from among noise or jamming pulses,we want to limit the number of non-EM pulses that are initiallydetected. These unwanted pulses can fill the buffer or restrictthroughput of the processor. In extreme cases, real EM pulses may not beable to be saved in the buffer or, if saved, there may be anunacceptable delay in processing all of the pulses to acquire the EMpulse train and perform the LOS calculation.

The basic trade-off is simple. Setting the threshold higher reduces thenumber of pulses that will be detected. This reduces both theprobability of a true EM pulse detection and the probability of a falsealarm. Conventional systems define acceptable maximum and minimumdetections (counts) over a fixed period, typically a few repetitionintervals, to maintain a CFAR. The processor counts the number of pulsedetections over the fixed period. At the end of the period, if the countexceeds the maximum the processor increases the CFAR Factor therebyincreasing the threshold. If at the end of the period, the count is lessthan the minimum the processor reduces the CFAR Factor to reduce thethreshold. If the count is between the minimum and maximum the CFARFactor is unchanged.

In the instant application this approach has two drawbacks. First,updates to the threshold are based solely on detected pulses at thecurrent threshold and not the characteristics of the entire signal.Second, the updates to the CFAR Factor are relatively slow. Inparticular, if a large number of pulses are detected (e.g. jamming) theprocessor will wait until the end of the period to adjust the CFARFactor. The buffer may be overwhelmed before the threshold reacts.Consequently, the detection thresholds are not sufficiently responsiveto either the noise characteristics of the individual channels or aburst of detected pulses.

To address these issues, the LOS processor suitably sets each channelthreshold as a function of the channel noise statistics and a modifiedCFAR algorithm that increases the CFAR Factor as soon as the max countis reached instead of waiting until the end of the fixed period.Similarly, the recovery from a high threshold can be accelerated byobserving sub-periods and if no pulses (more generally less than aminimum) are detected aggressively reducing the CFAR Factor. The noisestatistics are updated multiple times per pulse repetition interval. Thebase time scale (e.g. fixed period) of the CFAR Factor update is muchslower, typically ¼ to 2 PRIs. However, the actual time scale may becomparable since the algorithm will update the CFAR factor as soon asthe count exceeds the maximum and reset the fixed period. This approachreduces reaction time for a sudden change in noise level or pulsedetection frequency to less than one PRI. Furthermore, the sensitivityperformance is gracefully reduced with an increase in noise for a singlechannel of hardware. The all-digital LOS processor architecture providesthe platform for calculating the noise statistics and updating thethreshold in real-time and for modifying the CFAR algorithm.

In an embodiment, the detection threshold is set as follows:

Threshold(i)=Mean(i)+CFAR Factor(i)*Noise(i) for i=A, B, C, D and SumChannels where mean(i) is the mean or DC value of a set of samples usedto compute Noise(i). The CFAR takes into account issues that violate theGaussian noise model e.g. jamming, processor failure, etc., typicallyused to characterize a channel. If you assume a Gaussian noise modelthan the CFAR term may be replaced with a constant multiplier derivedfrom the model.

The benefits of adapting the detection threshold as a function of bothchannel statistics and a modified CFAR are illustrated in the simpleexamples shown in FIGS. 8 a and 8 c for a single PRI 130 between N andN-1 EM pulses 132 and 134 with channel noise 136. For purposes ofcomparison it is useful to note that the conventional CFAR algorithmthat evaluates the min and max counts at the end of a fixed period of acouple of PRIs would not update the threshold within the interval. Thedescribed approach updates the threshold multiple times within each PRIbased on the noise statistics and CFAR.

For purposes of illustration and example only, we assume that there are5,000,000 digital samples per pulse repetition interval 130 and that thenoise statistics are computed on every 13^(th) block of 1024 samples or375 times per pulse interval and assume that the CFAR fixed period is ½PRI. For example, a near IR laser source having a PRI of approximately 5ms with an A/D clock of 100 MBS produces about 5,000,000 samples perPRI. The noise is updated approximately every 0.1 msec. The CFAR periodmay be 2.5-10 msec but the CFAR factor may be updated at a fraction ofthe fixed period, possibly on the same time-scale as the noise. The PRI,CFAR period and the number of times the noise and CFAR are updated perPRI will depend on the source repetition interval, the A/D samplingrate, buffer capacity and processor speed. At a minimum, the processorwill update the noise multiple times per PRI and will be capable ofsub-PRI adjustments to the CFAR Factor. The CFAR period may be greaterthan one PRI but the processor can respond more aggressively.

FIG. 8 a illustrates the reaction of the threshold 138 to an increase inchannel noise 136. The noise statistic, hence threshold is updated veryrapidly so that the threshold tracks the rise and fall of the noise.This limits the number of false detections due to noise while preservingdetection of strong pulses such as 132 and 134 above the noise floor.

FIG. 8 b illustrates the reaction of the threshold 138 to the occurrenceof a burst of jamming pulses 140. The burst of jamming pulses quicklyexceeds the “max count” of the CFAR causing the threshold 138 to beincreased and the fixed period reset. In this example, the threshold isincreased and the period reset four times in less than one fixed periodat a rate proportional to the jamming pulse rate. This rapid reactionprevents most of the jamming pulses from being detected and thus fillingthe buffer and occupying the processor.

FIG. 8 c illustrates two different cases for the recovery of thethreshold after the burst of jamming pulses 140 and rapid increase inthe threshold 138. In the first case, which is not depicted by thisexample, there are pulses detected within the ½ PRI fixed period but thenumber of pulses due to the high threshold is less than the minimumcount at the end of the fixed period causing the CFAR factor, hencethreshold 138 a to be reduced. This is the conventional approach tolower the threshold. In the second case, which is depicted by thisexample, there are no pulses detected for a sub-period (½ the fixedperiod as shown here) because of the high threshold. Because no pulsesare detected over this sub-period, the processor reduces the CFARFactor, hence threshold 138 b sooner and more aggressively. Which of thetwo cases applies at any one time will depend on how high the thresholdwas raised, the noise characteristics and the pulse activity. Thisapproach provides for a more responsive recovery of the CFAR Factor.

An embodiment for updating the threshold for a channel is shown in FIGS.9 and 10. FIG. 9 illustrates the computation of the mean and noisestatistics and the updating of the threshold. FIG. 10 illustrates anembodiment for updating the CFAR Factor. Either or both processes may beimplemented in the embedded signal processor or in separate digitalprocessors. The noise, CFAR Factor and threshold are updated on allchannels simultaneously in real-time.

As shown in FIG. 9, the match filtered channel data is continuously fedinto the adaptive thresholding process. The first step is to remove allof the “pulse” samples that lie above the current threshold. In thisembodiment, this is accomplished by rejecting all samples on or 2 usafter a detected pulse (step 150). Every 13^(th) amplitude sample isaccumulated in a summation of 1024 samples and averaged to generate amean 151 (step 152). The mean is subtracted (step 154) from the next1024 samples and the average absolute value is calculated (step 156).The channel noise 158 is then computed as an approximated standarddeviation (1.25*mean(absolute(amplitude values−previous mean))) of the13*1024 samples after the estimated mean from the previous 13*1024samples is removed (step 148). The processor that receives this estimateof noise level can further filter it in time. The channel noise 158 ismultiplied by the CFAR Factor 160 (step 162) and added (step 164) to themean 151 to update the channel threshold 138. This is just one exampleof how the noise statistic may be calculated. The important point isthat a noise statistic is calculated for each channel and is updatedmany times per PRI so that the threshold reacts quickly to changes inchannel noise.

As shown in FIG. 10, the CFAR logic 170 has two inputs; the pulse count172 and CF_timer 174 (time since period was reset) and a number ofvariables 176 including the maximum count (Max_FAR), minimum count(Min_FAR), the fixed period (FAR_Period) and executes three separatetests to determine if the Max_FAR has been exceeded at any point withinthe FAR_Period, if the Min_FAR has not been reached at the end of theFAR_Period and whether no pulses have been counted in a sub-period (½the FAR_Period in this example) to output the updated CFAR Factor (CF).The CFAR variables are application dependent.

The embedded processor enters the CFAR logic to first determine whetherthe pulse count exceeds the Max_FAR (step 180). If yes, the logicdetermines whether the time is within the FAR_Period (step 182). If no,the logic exits. If yes, the CF is incremented by 1/16^(th) of thecurrent CF (step 184). The ratio can be set to other values. The logiclimits the CF to a Max_Limit (step 186), resets the CF_Timer (step 188),stores the CF to RAM (step 190) and exits the CFAR logic to output theupdated CFAR Factor 160.

If the pulse count does not exceed the Max_FAR, the logic determineswhether the FAR_Period has expired (step 192). If yes, the logicdetermines whether the pulse count is less than Min_FAR (step 194). Ifno, the logic exits. If yes, the CF is decremented by 1/16^(th) of thecurrent CF (step 196). The ratio can be set to other values. The logiclimits the CF to a Min_Limit (step 198), resets the CF_Timer (step 188),stores the CF to RAM (step 190) and exits the CFAR logic to output theupdated CFAR Factor 160.

If the FAR_Period has not expired, the logic determines whether adefined sub-period, here ½ the FAR_Period, has expired (step 200). Ifno, the logic exits. If yes, the logic checks to see if no pulses havebeen detected (step 202). If yes, the CF is decremented by ⅛^(th) of thecurrent CF (step 204). Essentially, if no pulses are detected over thissub-period, the logic more aggressively reduces CF in both time andamplitude. Again, the logic limits the CF to a Min_Limit (step 206),resets the CF_Timer (step 188), stores the CF to RAM (step 190) andexits the CFAR logic to output the updated CFAR Factor 160.

Enhanced LOS Processing

The all-digital LOS processor architecture provides a platform forenhanced LOS processing beyond simply accepting the four maximum valuesfor the individual quad channels captured upon detection of a pulse.Upon detection of a pulse, the memory controller stores a plurality ofsamples for each channel in memory. The plurality of samples comprisesall “pulse” samples with amplitudes above the threshold and a number ofleading and lagging samples with amplitudes below the threshold. In oneembodiment, the three leading samples are stored and the lagging samplesare stored until three in a row lie below the threshold. Depending onthe strength of the captured pulse and the threshold, these leading andlagging samples may capture pulse energy as well as noise. The processorestimates a pulse width and fits a curve to the stored amplitude samplesbased on logic and a scale factor dictated by the pulse width toestimate a peak amplitude for each individual channel and calculates aLOS to the target from the estimated peak amplitudes. The processor isconfigured to select the strongest SNR channel (used to detect pulses)to determine the scale factor, the samples processed and the controllinglogic. These parameters are then applied to all individual channels. Theresult is that the estimated peak amplitudes more accurately representthe peak amplitudes of the sampled analog pulse waveforms, hence the LOScalculation is more accurate.

As shown in FIGS. 11 a-11 b and 12 a-12 b, the embedded processor isconfigured to perform enhanced LOS processing on the unfiltered digitaldata on the individual channels. The processor removes the DC portionfrom the raw digital data (step 210) and identifies the strongestchannel for each pulse (step 212). The strongest channel may have beendetermined upon initial pulse detection and saved as an index or bysimply selecting the channel with the largest SNR. In this example, thereturned EM pulse spot 214 lies primarily in Channel D of quad-detector216, hence Channel D is the strongest. The processor may recreate theprimary and/or secondary sum channel(s) from the individual channelseither to determine the strongest channel or, if designated by the indexas a sum channel, to use the selected sum channel to determine the pulsewidth, samples processed and logic employed.

As shown in FIG. 12 a, forward sample A1, pulse samples A2, A3 and A4and trailing sample A5 that sample an EM pulse 218 above and below apulse width threshold 220 (e.g. 6 dB down from the max sample amplitude)are stored in memory. The processor calculates a pulse width 222 for thepulse (step 224). A known way to estimate pulse width is to simplymultiple the number of pulse samples by the sampling period. As shown inFIG. 12 b, when computed for a large number of pulses this produces apulse width estimate 226 that is neither accurate nor consistent. Apreferred approach is to use the amplitudes of both the pulse samplesand weighted amplitudes of the forward/trailing samples to estimate thepulse width as the ratio of the pulse power to the peak energy. Forexample, pulse width=((A1/2)+A2+A3+A4+(A5/2))*T/A3 where T is thesampling period. In this example, the contributions of the forward andtrailing samples are weighted by a factor of ½. The pulse widthestimates 228 are more accurate and more consistent.

Note, the pulse and forward/trailing samples are not necessarily alignedwith the pulse and leading/lagging samples referenced to the detectionthreshold 83, which may be higher or lower than threshold 220. Thosesamples are stored in memory for each channel to create a pool ofsamples from which to calculate the pulse width. The forward andtrailing samples are likely selected from the pool of leading/laggingsamples but not necessarily. If the pulse is very strong and/or thedetection threshold relatively low, the forward/trailing samples mayactually lie above the detection threshold.

Using the estimated pulse width to determine a scale factor, theprocessor performs a curve fit to the stored samples (step 230). Thecurve fit may be a conventional linear curve fit to an N^(th) orderpolynomial or an alternate linearization of a curve fit algorithmdetailed in FIG. 13. For the same number of calculations, the latterapproach provides a more accurate estimate of the peak. Essentially, thelatter approach looks at the two samples with the largest amplitudes forthe strongest SNR channel. If one amplitude is considerably greater thanthe other, the larger amplitude is probably very close to the peakamplitude of the underlying analog signal. As shown in FIG. 12 a, A3 isconsiderably larger than A4. In this case, either the larger amplitudeis accepted as the estimate of the peak or increased by a small scalefactor. If the two amplitudes are close, the peak amplitude of theunderlying analog signal lies approximately at a mid-point in timebetween the two samples. In this case, the larger amplitude is adjustedby a larger scale factor. For example, if sample A3 did not exist thenA2 and A4 are very close and A4 would be adjusted by a large scalefactor to estimate the peak of the underlying pulse 218 lyingapproximately at their mid-point in time. The strongest channel is usedto select the two samples with the largest amplitudes, select averaginglogic or the described curve fit logic and to provide the scale factorfor the curve fit logic.

The processor applies the curve fit to each channel to estimate fourpeak amplitudes (step 232). The curve fit is applied to the same samplesin all channels as dictated by the curve fit to the strongest SNRchannel. These samples may or may not be the same time samples thatwould have been selected if each channel were curve fit independently.The processor adjusts the individual channel gains to balance thechannels (step 234) and computes the Pitch_LOS and Yaw_LOS as functionsof the estimated peak values (step 236). The Pitch_LOS is given by((C+D)−(A+B))/(A+B+C+D) and the Yaw_LOS is given by((A+D)−(B+C))/(A+B+C+D). The Yaw and Pitch LOS pair is an excellenterror signal to drive an active control loop for centering the detectorfield of view on the EM signals' physical position (bore-sighting).However, because of non-uniformities and asymmetries in the detector,Yaw and Pitch LOS does not perform optimally as a true Pitch and Yawmeasurement since the slope of LOS versus true angle changes as afunction of angle. The non-linear transfer function of true angle versusthe LOS measurements is mapped in a final AZ/EL LOS table (step 238).The Pitch and Yaw LOS measurements are indexes into the table lookup andthe output of the table is corrected Azimuth (AZ) and Elevation (EL)angles which is then output in the target report 58.

An embodiment of the logic for the linearization of the curve fitalgorithm is shown in FIG. 13. The strongest SNR channel is used toselect the two time samples from which the peak is estimated, to selectthe logic for estimating the peak based on the pulse width calculatedfrom the strongest channel and to set the scale factor for the expandedcurve fit option. The individual channels provide only their specificamplitudes for the two selected time samples.

The embedded processor first compares the pulse width measured on thestrongest channel against a threshold e.g. 20 ns (step 240). If thepulse is narrow, a scale term is set to a constant e.g. 53 (step 242).If the pulse is narrow, the scale term is scaled up from the constant toa maximum value e.g. 64 (step 244). For example, Scale=(PW−32)/5+53where PW is measured in nanoseconds with a scale factor of 8/5 applied(i.e. a value of 32 represents 20 ns). The thresholds and constants hereare only exemplary for a specific application.

The processor determines whether there are multiple samples that exceedthe threshold on the strongest SNR channel (step 246). If not, the indexS_(peak) is selected and used for each channel e.g.A_(i)=A_(i)(S_(peak)) for i=1 to 4 (e.g. channels A, B, C, D) (step248). The pulse time of arrival of sample S_(peak) is forwarded to thetime-correlation and tracking routines as an offset from the firstdetected pulse on any channel in order to track based on the peak sampleof the strongest channel. If yes, the processor identifies the strongestchannel's highest and next highest amplitude values and saves theirsample indices S_(max) and S_(next), respectively (step 250). The pulsetime of arrive for sample S_(max) is forwarded. Note, the strongestchannel may be any one of the four individual channels or any sumchannel.

Once the two sample indices S_(max) and S_(next) are selected, the logicallows each individual channel to determine which sample corresponds tothe highest and next highest amplitude values e.g. flip the indices. Thevalue A_(i) is set equal to the maximum amplitude for the pair ofindices and a value A_(temp,i) is set equal to the minimum amplitude(step 252). The same two indexed samples are used for all channels toprevent a larger than normal noise sample from being selected as thepeak on a channel with a weak signal and distorting the amplitudemeasurement. The hardware time alignment from channel-to-channel issubstantially less than one time sample to support this decision.

The processor then checks to see whether the pulse width of thestrongest SNR channel is less than a second higher threshold e.g. 50 ns(step 254). The threshold is set based on knowledge of the sampling timeinterval of the A/D converters to distinguish wide pulses with multiplesamples near the peak from narrow pulses. If the pulse width is greaterthan the threshold of 50 ns, the sampling interval is such that thereshould be two samples per channel that are near the peak of the pulsewith a small amplitude difference relative to the expected noisemagnitude. In this case, the sample with the larger amplitude has almosta 50% probability of actually being the smaller amplitude sample withmore noise. If the pulse width is less than the threshold, the samplinginterval is such that there can only be one sample near the peak ofpulse. The strongest channel via the pulse width measurement determinesthe logic used to form the peak estimate; averaging logic for broadpulses and a linear curve fit logic for narrow pulses.

Therefore if the pulse width is greater than the threshold, the averagelogic dictates that the polynomial coefficients are 0.5 and 0.5, henceA_(i) is calculated as the mean of A_(i) and A_(temp,i) (step 256) foreach channel. The average gives a better estimate of the peak for widepulses by reducing the variation from noise. The average is performed onthe same two time samples for each channel, so if there is an offsetfrom the peak produced, it shows up equally on all channels.

If the pulse width is less than the threshold, the curve fit logicexpands the equations so that the peak estimate is1.5*A_(i)−(Scale/128)*A_(i) ²/A_(temp,i) where Scale is chosen by thepulse width estimate of the strongest SNR channel in step 242 or 244.Instead of automatically applying the expanded curve fit equation toeach channel, which could be done, the processor performs differentintermediate tests on each channel to account for the effects of noise,particularly on relatively weak signals. The processor determineswhether A_(temp,i) has a positive value (step 258). If not, theprocessor determines whether A_(i) has a positive value (step 260). Ifyes, the processor sets A_(i)=A_(i) (step 262). If A_(i) is less thanzero the processor sets A_(i) to zero (step 264). This determination ismade for each channel. If the EM pulse signal on the channel is weak orthe noise high, one or both amplitude samples can be negative and thechannel peak will be determined with this logic. The processor alsochecks to see if all resulting peak amplitudes are negative (not shown).If the amplitude from the strongest channel comes out of thesecalculations as negative then it is assumed that there is a problem withcalibration constants that are stored in the processor that created anegative offset on all data or that something in the processor hasmalfunctioned. In this case the smallest channel amplitude (mostnegative value) is subtracted from all the channel amplitudes, so theminimum value becomes zero and the remaining channel amplitudes areshifted to be positive.

If A_(temp,i) is positive, the processor computes a fit parameterFITi=SCALE*A_(i)/A_(temp,i) (step 266). The curve fit is scaled to havea max FITi of 64 for the narrowest expected pulse when sample A_(i) isat the peak assuming no noise and a min FITi of 53 for broad pulses whenthe peak lies half-way between samples A_(i) and A_(temp,i). Noise cancause a channel to exceed this value by either increasing A_(i) ordecreasing A_(temp,i). If FITi is not less than the max of 64 (step 268)the processor again simply selects the max amplitude and setsA_(i)=A_(i) (step 270). The processor could be configured to skip step268 and apply the curve fit equation regardless. If FITi>64, the curvefit equation will reduce the max sampled value A_(i) on the assumptionthat the sample was inflated by noise. However, FITi is more sensitiveto A_(temp,i) being reduced by noise because it is in the divisor.Simulations have shown that clipping FITi at the max value of 64 andusing the max sampled amplitude A_(i) improves the accuracy of the LOScalculation. This test is applied per channel.

If FITi<64, the processor scales A_(i) as follows:A_(i)=[1+(64−FITi)/128]*A_(i) in accordance with the curve fit equationsto estimate the peak (step 272). If FITi is close to the max value of 64the max sampled value A_(i) is increased only a small amount. If FITi isclose to the min value of 53 the max sampled value A_(i) is increased bya maximum amount. In other words, if the time sample S_(max) is close tothe actual peak of the underlying analog waveform and the pulse isnarrow, the amount of correction if any is very small. If the timesamples S_(max) and S_(next) occur at approximately ½ sampling period Ton either side of the actual peak, the amount of correction isrelatively large. For cases between these two extremes the curve fitequation varies the amount of correction.

The all-digital LOS processor architecture provides flexibility toaddress special cases that may arise such as dropped channels or channelsaturation. As shown in FIG. 14, the LOS processor can tolerate andadapt the LOS calculation for a single bad channel 300. One way todetermine whether a channel is bad is to compare the channel noise(computed for the adaptive thresholding) to the channel noise of theother three channels. If the channel with the highest absolute deviationfrom the root-mean-squared (RMS) of the other three channels does notlie within upper and lower deviation limits it is declared a badchannel. The LOS calculations are adapted as follows:

-   -   Case 1. A Channel Missing        Pitch_LOS=(C−B)÷(C+B)        Yaw_LOS=(D−C)÷(D+C)    -   Case 2. B Channel Missing        Pitch_LOS=(D−A)÷(D+A)        Yaw_LOS=(D−C)÷(D+C)    -   Case 3. C Channel Missing        Pitch_LOS=(D−A)÷(D+A)        Yaw_LOS=(A−B)÷(A+B)    -   Case 4. D Channel Missing        Pitch_LOS=(C−B)÷(B+C)        Yaw_LOS=(A−B)÷(A+B)

As shown in FIG. 15, if any one of the channels saturates (i.e. one ormore samples 302 are above the saturation threshold 304 indicating thoseamplitudes have put the receiver hardware in compression or saturationand are therefore inaccurate) the curve fit algorithm is bypassed andthe first unsaturated sample 306 prior to saturation is used as the peakestimate. The same time sample is used for each channel regardless ofwhether those channels are saturated or not. Although this approachlowers the SNR it still provides accurate LOS estimates becausepresumably to saturate the EM pulse, hence the first leading sampleamplitude must be strong. This approach effectively extends the dynamicrange of the LOS processor beyond the inherent limit of the receiverhardware.

Countermeasures

Broadly defined, countermeasures encompasses different techniques todifferentiate “jammer” pulses from EM pulses from the known source andremove the jammer pulses as far upstream as possible to avoidoverwhelming the memory or processor.

The Jammer is typically an EM source that is pointed at the ground atsome standoff distance from a potential target. Consequently, theamplitude and the LOS of the reflected jammer pulses are very similar tothe reflected source EM pulses. The mission of the jammer is not toobfuscate the real EM pulses but to either cause the processor toacquire and pursue a false track on the jammer pulses and/or to simplyoverwhelm the buffer and processor. Because memory and processor speedis available and inexpensive, the jammer must transmit pulses at a veryhigh repetition rate in order to confuse or overwhelm the LOS processor.

As shown in FIG. 16, the PRI/Time correlation and PDL processing used toacquire and track a target (FIG. 7 a) is augmented with certaincountermeasures processing to differentiate the jammer pulses 320 fromthe EM source pulses 322 and remove them from the pulse train. Theprocessor determines the strongest SNR channel for each pulse (step 109)and performs Pulse Discrimination 116 after the Notch Filter 324 andTime Correlation 110 processing. Because of the LOS similarity thestrongest channel is most likely to be the same for the jammer and EMsource pulses. The processor passes the pulse train through a notchfilter 324 that is configured to remove pulse trains (single-frequencyor pseudo-random) that occur at frequencies (repetition rates) greaterthan an octave above a known repetition rate of the EM source pulses.The notch filter is a simple technique to remove the bulk of the jammerpulses that might other-wise overwhelm the memory or processor.

PRI/Time correlation 110 is performed on the notch filtered pulse trainthat includes the EM source pulses 322 and any remaining jammer pulses320 within an octave of the source frequency. Most of these jammerpulses do not pass the PRI/time correlation criteria unless theirfrequency is very close to the actual laser frequency, close enough thatthe processor thinks the jammer pulses are the EM source pulses. In someinstances, jammer pulses that occur at approximately the same time as anEM pulse cannot be removed by the notch filter and appear in thetime-correlation track gate with the EM pulse. The PDL 116 extractsfeatures such as pulse width, rise/fall, time etc. for each of the oneor more pulses 320 and 322 in the track gate 326. In addition to thesestandard criteria for determining whether a single pulse is or is not anEM source pulse or in the event that the standard criteria cannotdistinguish the EM source and jammer pulses, the processor appliesmultiple pulse selection criteria 328 that look at the positions of thepulses in the track gate and the relative amplitudes of the pulses. Theprocessor can be configured through an interactive digital userinterface to expect the EM source pulse to have the largest amplitude,lie closest to the center of the track gate or be the first or lastpulse within the track gate. The processor can use this information toeither select a pulse directly or to augment the PDL to select a pulse.Once the target is acquired, the processor enters track mode 118. Theprocessor places and updates 120 track gate 326 (e.g. narrows the gate)to capture the next EM pulse. The placement and narrowing of the gatemakes it more difficult for jammer pulses to survive and eliminates theneed to perform multiple pulse selection. If the track ‘breaks’, theprocessor will widen the track gate and restart the time-correlation.The initial width of the track gate is suitably set by the worst casetime error of the EM source.

The technique for estimating the pulse width based on the ratio of thepulse energy to the peak power for the strongest SNR channel provides amore accurate pulse width measurement that can be used to improvedifferent countermeasures. First, the improved pulse width measurementimproves the upstream matched filtering of the digital signals. Thisfurther differentiates the EM source pulses from the jammer pulsesmaking the removal of the jammer pulses simpler. Second, the improvedpulse width measurement becomes a more powerful and reliable feature forthe PDL to differentiate EM source pulses from jammer pulses. Lastly, ifthe pulse width is more accurately known the track gate can be sized andpositioned more accurately thereby better excluding jammer pulses. Theeffect of PW accuracy on the gate will become more important as EMsources achieve reference oscillator performance that allows pulseinterval stabilities to approach the width of a pulse.

The notch filter is particularly effective when used as a front-endprocess for adaptive thresholding based on both the CFAR and channelnoise. If the notch filter effectively removes jammer pulses, thethreshold processor will optimize the threshold to detect EM pulses toacquire and track the target. The removed jammer pulses are treated asif they never occurred and thus do not effect the CFAR Factor orthreshold. If the notch filter cannot remove jammer pulses, thethreshold process will react very quickly (e.g. much less than onerepetition pulse interval of the EM pulses), treating the jammer pulsesas noise or false-alarms, to raise the threshold to limit furtherdetection of jammer pulses.

As shown in FIG. 17 a, an embodiment of a notch filter to counter asingle-frequency jammer looks for a three pulse sequence 330 thatsatisfies two criteria. First, are the three pulses equally spaced intime? Second, is that spacing less than half the minimum expectedrepetition interval of the EM source pulses? If both criteria aresatisfied, the three pulse sequence is acquired as a jammer sequence anddeleted from the acquisition buffer. After a tone (3 pulses) isidentified the next time of arrival (with a notch gate 332) is projectedinto the future. If a pulse 334 is detected in the notch gate it isdeleted from the acquisition buffer. The time of the last jammer pulseis used to update the expected time interval to project the next pulseand so on. The dashed lines indicate jammer pulses that have beenremoved. The solid lines indicate EM source pulses 336 that are notremoved by the notch filter, which are then captured by thetime-correlation process to acquire the target and to track sourcepulses that fall within the next track gate 338.

The jammer pulses are detected and stored in memory, but the notchfilter processing is running at a speed that allows it to find thejammer with its first 3 pulses before the pulse memory can come close tofilling up and before enough pulses are detected to trigger the CFARpulse count that would cause an increase in the detection threshold. Thejammer pulse rate is accounted for in the CFAR algorithm, whichsubtracts the expected number of jammer pulses from the total pulsecount to get a count of non-notched pulses for the CFAR decision. Afterthe notch filter locks on to a jammer, the notch filter runs fast enoughto remove the jammer pulses one at a time before the next one arrives,so there is no more than one jammer pulse in the pulse buffer at anytime. If the jammer was producing pulses fast enough to trigger the CFARdecision logic the jammer pulses would be accounted for and no thresholdchange would occur. The CFAR pulse count threshold is normally set toallow hundreds of jammer or noise pulses within one source pulserepetition interval (excluding some RF and MMW applications with a highpulse repetition frequency) since the time-correlation can handle a highnoise detection rate before failing.

The notch filter only looks for pulse intervals that correspond togreater than an octave of frequency above the highest EM source pulsetone. Consequently, the notch filter does not effect the EM source pulsetrain directly (i.e. the notch processing does not lock onto thesource's first harmonic). It is possible for an EM source pulse to fallat exactly the wrong point to confuse the notch filter, such that the EMsource pulse is used as one of the 3 pulses to define the notched jammersignal. This is unlikely, less than a small percentage for the worstcase when the jammer is right at the lowest acceptable jammer frequency,based on the size of the notch gate 332. Higher jammer frequencies havea lower probability of using an EM source pulse in the acquisition ofthe jammer because there are more jammer pulses within a source pulseinterval. When an EM pulse is used to define a jammer then byrestraining the frequency to greater than an octave above the EM sourcefrequency then the next expected jammer pulse (fourth in the series)cannot be an EM source pulse given the EM source conforms to theexpected pulse interval and maximum interval variation. This fourthpulse event will realign the expected jammer pulse sequence with theobserved jammer time sequence. Also, the jammer pulse train is trackedby the notch filter so if it cannot be maintained (continued to betracked), because of the error created in the defined jammer pulseinterval by mistakenly using a source pulse, then the jammer track isaged out (eliminated) and the notch processing will have to re-lock ontothe jammer. Only one source pulse is lost in this event which istolerated by the tracker. After a jammer is declared then a smallpercentage of source pulses are expected to be eliminated by the notchprocessing as it continues to filter out detections and this is alsotolerated by the tracker.

As shown in FIG. 17 b, an embodiment of a notch filter is configured tocounter a jammer that transmits a pseudo-random pulse train. The jammerrepeatedly transmits a string 340 of N pulses 342 (where N=3 in theexample) that are randomly spaced within the string. In effect thiscreates N=3 pulse trains at the same frequency that are randomly shiftedin time with respect to each other. The processor again looks for threepulse sequences that satisfy the spacing and frequency criteria to lockonto each of the N pulse trains. The implementation is a bit trickier toseparate the multiple jammer pulse trains.

The processor creates a base pointer, for example to pulse #1 time ofarrival (TOA). A second pointer starts at TOA #2 and a third points atTOA #3. The third pointer is incremented upward looking at the timedifference between the pointer #3 and pointer #1 TOA's. If this timedifference is less than half the expected laser PRI then the time ofPointer #2 is checked to see if it is midway between the two end times.If it is close to the center then a jammer track is begun. If it is notclose then pointer #2 is incremented and another check of the mid pointis made. This continues until pointer #2 points to a time past the midpoint or until pointer #2 equals pointer #3, at which pointer #3 isincremented. Pointer #3 and #2 are moved in this manner until thedifference between the time of Pointer #3 and Pointer #1 becomes greaterthan half the expected laser PRI and at this point the base pointer #1is incremented and so on. This process is terminated after a set numberof pulses have been processed. It is also repeated after a jammer isdefined to try and establish track on another jammer from any pulsesthat are not eliminated by the previous defined jammer tracks. Therepeat is terminated after a set number of jammers are defined. In thisexample, the processor acquires and removes three jammer tones leaving atrain of EM source pulses 344.

This might appear to be more time consuming than just trying tocorrelate the time between the desired laser pulses, since the EM sourcetime interval is known and this allows the pointers to be moved moreeffectively, but the jammer process is only executed once. If a jammeris tracked then the expected next pulse time of arrival for a jammerpulse is calculated, allowing the next pulse that arrives to be comparedto this time and thus get removed in near real time without theprocessor having to store (keep) more than one jammer pulse sample. Thismakes for a more effective use of memory (it does not get filled up withjammer pulses before the required number of source pulses are receivedfor correlation) and it produces a quicker source correlation processwith fewer pulses to process which compensates for the time spentsearching for jammers.

A more detailed presentation of the pulse acquisition and trackingprocess is illustrated in FIGS. 18 a and 18 b assuming the notch filteris running and all pulses in the pulse buffer are sequenced through toacquire and track the EM pulse source.

FIG. 18 a depicts the functional transitions and data flow to acquireand track an EM pulse train. Signal search is begun (step 356) and asequence of pulse Time of Arrivals that matches the expected Source timeintervals constitutes a time correlation (step 345), which is followedby the calculation of a correlation error comprised of the pulseparameter errors (not shown) and time correlation error (TOA_Error). Ifthis correlation error is below the EM Source specific correlationthreshold then the laser is acquired and Track is begun (step 355).Three or more pulses can be used in the acquisition sequence and uponacquisition of the EM Source the adaptive gate parameters (Gate,Gate_Sum and Count) are initialized (step 346) and the parameters thatconstitute the track gate (T_Begin and T_End) for the next expectedpulse are initialized (step 354).

Thereafter the Count, Gate_Sum, Gate, T_Begin and T_End parameters areupdated for each subsequent tracked pulse to allow the track Gate width370 to adapt (step 349) and either collapse or expand based upon themeasured Time of Arrival error (step 357) as depicted in FIG. 18 b.

While the track process waits for the next detected pulse, time ismonitored to ensure the T_End time is not reached (step 347). If theprocessor clock indicates that time exceeds T_End while waiting for apulse detection then “No Pulse” is declared and the track processor willcoast to the next expected pulse (step 348). The coast process extendsthe expected TOA to the next expected pulse by adding the current PulseRepetition Interval to both T_Begin and T_End. However, if the timesince the last track pulse was detected has been too long (as measuredby T_Begin minus the last tracked TOA) then track of the EM Source isterminated (step 348).

While waiting for a pulse detection event (step 347) and while timeremains less than T_End and if a pulse is detected then the TOA for thepulse is compared to T_Begin and T_End. If the TOA is greater than T_Endthen the track process will coast to the next expected Time of Arrivalor terminate track when the time since the last track pulse has been toolong (step 348). If the TOA of the detected pulse is less than T_End andgreater than T_Begin it is accepted based upon time correlation.

Before declaring the pulse as the next tracked pulse it must also passother pulse discrimination constraints. To facilitate arbitration ofmultiple pulses within a track gate a track record that containsestimated pulse parameters is maintained by the track processor. Line ofSight, Pulse Width, Rise Time, Fall Time, next TOA Estimate andEstimated Amplitude are tracked (not shown) for each EM signal undertrack. These estimated pulse parameters are used to arbitrate betweenmultiple pulses that fall in the same track gate (step 353). Byimplementing the arbitration function after time correlation, multipletrack correlation schemes can be selected to arbitrate to a single pulsefor the tracker update (step 358).

The pulse arbitration can be set to eliminate pulses that have measuredpulse parameter errors above set thresholds when compared to theparameter estimates maintained in the track record. Also the pulsearbitration can be set to select the pulse with the smallest combinedcorrelation error (step 359).

When the combined correlation error arbitration is not used then forarbitration of multiple pulses that remain after the parametercomparisons the arbitration can be set to select the pulse based uponrelative position within the gate (step 353). The closest to the trackgate center or the last pulse or the first pulse or the pulse with themaximum amplitude can be chosen per user setup prior to beginning thesignal acquisition process.

When a pulse has been accepted in the arbitration process, the pulseparameters are updated and stored, but the arbitration process continuesto evaluate future pulses for a better correlation error or a betterposition within the track gate (step 347). A better correlation error orposition when compared to the previous best result is then saved as thelast best correlation pulse. As the processor waits for the next pulsedetection, if the processor time exceeds the T_End value indicating thatno future pulses will correlate in time with the current track gate thenthe last best correlated pulse is used for the Tracker update.

The tracker update calculates the TOA_Error step (357) as the differencebetween the expected TOA (TOA_est) and the measured TOA for the currentcorrelated pulse. This error is used to update the Gate width (step 349)and the TOA_est (step 350) for the next expected pulse arrival time. Thetrack window parameters (T_End and T_Begin) are updated for the nextpulse correlation process (step 351), the Line of Sight measurement isoutput (step 352) and the time correlation process is repeated to searchfor the next EM source pulse (step 347) to update and continue the trackprocess.

While several illustrative embodiments of the invention have been shownand described, numerous variations and alternate embodiments will occurto those skilled in the art. Such variations and alternate embodimentsare contemplated, and can be made without departing from the spirit andscope of the invention as defined in the appended claims.

1. A digital line-of-sight (LOS) processor for detecting electromagnetic(EM) pulses reflected off a target from a source that emits the EMpulses at known pulse repetition intervals and computing a LOS to thetarget, comprising: a plurality of A/D converters configured to convertrespective analog signals from a multi-channel detector into a pluralityof digital signals on respective individual channels; a digital summerconfigured to sum a plurality of digital channel signals to produce adigital signal on a sum channel; at least one digital pulse detectorconfigured to compare sample amplitudes of the digital signal on atleast the sum channel to a detection threshold to detect pulses; afilter configured to remove pulses that occur at repetition ratesgreater than an octave above the repetition rate corresponding to theknown pulse repetition interval; a memory; a memory controller that upondetection of a pulse by any one of the one or more pulse detectorsstores a plurality of samples for each said individual channel in saidmemory; and a processor configured to process the sample amplitudes forthe individual channels to calculate a LOS to a target.
 2. The digitalLOS processor of claim 1, wherein the filter removes three pulsesequences that satisfy a first criteria that the three pulses areequally spaced in time within a tolerance and a second criteria that thespacing is less than half the known pulse repetition interval.
 3. Thedigital LOS processor of claim 2, wherein the filter is configured toremove single-frequency pulses.
 4. The digital LOS processor of claim 2,wherein the filter is configured to remove a pseudo-random pulse train.5. The digital LOS processor of claim 2, wherein once the filteracquires a track on a three pulse sequence that satisfies the first andsecond criteria, the filter looks for a next pulse within a notch gatecentered at the spacing ahead of the last pulse, if a pulse is detectedin the notch gate it is removed and the process repeated until the trackis lost.
 6. The digital LOS processor of claim 1, wherein the processoris configured to perform a time correlation of the remaining detectedpulses to the pulse repetition interval to down select to one or morecandidate sequences of pulses, extract discrimination parameters fromeach of the pulses in the candidate sequence, and evaluate thediscrimination parameters and time-correlation errors for the candidatesequences to acquire and track a target sequence of pulses to calculatethe LOS.
 7. The digital LOS processor of claim 6, further comprising aplurality of said digital pulse detectors configured to compare sampleamplitudes of the digital signal on the sum channel and the individualchannels to respective detection thresholds to detect pulses, saidprocessor configured to determine a strongest SNR channel for each pulseand perform the time correlation on the detected pulses in the strongestchannels and extract discrimination parameters from the pulses in thecandidate sequences from the strongest channels.
 8. The digital LOSprocessor claim 7, wherein the processor is configured to combine thediscrimination parameters for individual pulses, joint discriminationparameters for pulses across the candidate sequence and time correlationerrors into an association error that is evaluated to accept or rejectthe candidate sequence.
 9. The digital LOS processor of claim 8, whereinthe joint discrimination parameters comprise uniformity metrics on thediscrimination parameters for individual pulses across the candidatesequence.
 10. The digital LOS processor of claim 8, wherein theprocessor is configured to place a track gate at the pulse repetitioninterval ahead of the last detected pulse for the target sequence toacquire the next detected pulse, said embedded processor adapting andnominally reducing the width of the track gate as the number of trackedpulses in the target sequence increases.
 11. The digital LOS processorof claim 6, wherein the processor is configured to place a track gate atthe pulse repetition interval ahead of the last detected pulse for thetarget sequence to acquire the next detected pulse, if multiple detectedpulses lie within the track gate the embedded processor can eliminatepulses based upon discrimination parameters and select from theremaining pulses (a) the pulse having the largest amplitude, (b) thepulse lying closest to the center of the track gate or (c) the lastpulse or the first pulse.
 12. The digital LOS processor of claim 1,wherein the detected pulses include pulse samples with amplitudes abovethe threshold and noise samples with amplitudes below the threshold,further comprising: at least one threshold processor paired to said atleast one digital pulse detector, each said threshold processorconfigured to process the noise samples on the channel to estimate noiseand update the detection threshold multiple times per pulse repetitioninterval as a function of the channel noise.
 13. The digital LOSprocessor of claim 12, wherein each said threshold processor isconfigured to update the detection threshold as a function of thechannel noise and a constant false-alarm rate (CFAR) factor.
 14. Thedigital LOS processor of claim 13, wherein said embedded processor isconfigured to increase the CFAR factor as soon as a pulse count exceedsa maximum count defined for a fixed period and reset a count period. 15.The digital LOS processor of claim 14, wherein said embedded processoris configured to reduce the CFAR factor if the pulse count is less thana minimum count at the end of the fixed period and more aggressivelyreduce the CFAR factor if the pulse count is zero for a definedsub-period less than said fixed period.
 16. A digital line-of-sight(LOS) processor for detecting electromagnetic (EM) pulses reflected offa target from a source that emits the EM pulses at known pulserepetition intervals and computing a LOS to the target, comprising: aplurality of A/D converters configured to convert respective analogsignals from a multi-channel detector into a plurality of digitalsignals on respective individual channels; a digital summer configuredto sum all of the digital channel signals to produce a digital signal ona primary sum channel; a plurality of digital pulse detector configuredto compare sample amplitudes of the digital signal on the primary sumchannel and individual channels to respective detection thresholds todetect pulses including pulse samples with amplitudes above thethreshold and noise samples with amplitudes below the threshold; afilter configured to remove pulses that occur at repetition ratesgreater than an octave above the repetition rate corresponding to theknown pulse repetition interval; a plurality of threshold processorspaired with said plurality of digital pulse detectors, each saidthreshold processor configured to process the noise samples of theassociated channel to estimate noise and update the detection thresholdof the paired pulse detector as a function of the noise; a memory; amemory controller that upon detection of a pulse by any one of the oneor more pulse detectors stores a plurality of samples for each saidindividual channel in said memory; and a processor configured to processthe sample amplitudes for the individual channels to calculate a LOS toa target.
 17. The digital LOS processor of claim 16, wherein the filterremoves three pulse sequences that satisfy a first criteria that thethree pulses are equally spaced in time within a tolerance and a secondcriteria that the spacing is less than half the known pulse repetitioninterval.
 18. The digital LOS processor of claim 16, wherein each saidthreshold processor is configured to update the detection threshold as afunction of the channel noise and a constant false-alarm rate (CFAR)factor.
 19. The digital LOS processor of claim 16, wherein the processoris configured to determine a strongest SNR channel for each pulse andperform a time correlation of the remaining detected pulses to the pulserepetition interval in the strongest SNR channel to down select to oneor more candidate sequences of pulses, extract discrimination parametersfrom each of the pulses in the candidate sequence in the strongest SNRchannel, and evaluate the discrimination parameters and time-correlationerrors for the candidate sequences to acquire and track a targetsequence of pulses to calculate the LOS.
 20. The digital LOS processorof claim 16, wherein said processor is configured to determine whichchannels are likely to have relatively high and low SNR and selectivelydeactivate the digital pulse detector for one or more of the individualchannels or primary and secondary sum channels having relatively lowSNR.